Keeping AI working

Summary

The most important healthcare AI story recently is not another model launch. It is governance.

Content

The American College of Radiology’s new imaging AI practice parameter matters because it asks the right question: not “does this AI work somewhere?” but “does this AI keep working here, for our patients, in our workflow, over time?”

That is the real test for clinical AI.

• Applicability: Was the tool trained and validated for the patients, scanners, settings and clinical decisions where it will actually be used?
• Accuracy: Is performance monitored after deployment, not just at procurement? Does anyone know when the model drifts?
• Acceptability: Do clinicians trust it, understand its role and know when to override it? Do patients know when AI is involved?
• Accountability: Who owns the decision when AI flags, misses, prioritises or misclassifies?

This is where healthcare AI becomes serious.

The future will not be won by the hospitals with the most algorithms. It will be won by the hospitals with the best operating model for safely using them.

The practical questions now are:

• Can we monitor AI like we monitor infection rates, readmissions or surgical outcomes?
• Can we make model drift visible before it becomes patient harm?
• Can we prove local value, not just vendor accuracy?
• Can we design AI systems clinicians actually accept because they are useful, safe and accountable?

That is the shift from AI hype to AI healthcare infrastructure.

About the Author

Richard A D Jones is an AI strategy and healthcare innovation specialist, and AI hub topic lead for Patient Safety Learning. He has worked across AI, healthcare, telecoms and transformation on five continents, advising boards, supporting startups and helping organisations turn digital health ideas into practical impact.

His work focuses on using AI safely and effectively to improve outcomes, support better decision-making and reduce avoidable harm.